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Fluorescence Lifetime Imaging (FLIM) is a powerful technique that measures the decay time of fluorophores present in tissue samples alluding to their constituent molecules. FLIM has gained popularity in biomedical imaging for applications such as detecting cancerous tumors, surgical guidance, etc. However, conventional FLIM systems are limited by a reduced number of spectral bands and long acquisition time. Moreover, the large footprint, complexity, and cost of the instrumentation make it difficult for clinical applications. In this paper, we demonstrate a reconstruction-based hyperspectral detector that can resolve decay time and intensities in broad spectral ranges while providing high sensitivity, high gain, and fast response time. The hyperspectral detector is comprised of an array of efficient, ultrafast avalanche photodetectors integrated with nanophotonic structures. We utilize different nanostructures in the detectors to modulate light-matter interactions in spectral channels. This allows us to computationally reconstruct the spectral profile of the incoming fluorescence spectrum without the need for additional filters or dispersive optics. Also, the nanophotonic structures enhance efficiency (by a factor of 2 to 10 over different wavelengths) while providing fast response time. An innovative detector design has been employed to reduce the breakdown of the avalanche photodetectors to -7.8V while maintaining high gain (~50) across the spectral range. Therefore, enabling low light detection with a high signal-to-noise ratio for FLIM applications. Added spectral channels would provide valuable information about tissue materials, morphology, and disease diagnosis. Such innovative hyperspectral sensors can now be integrated on-chip capable of miniaturizing the FLIM system and making it a commercially viable tool for clinical use. This technology has the potential to revolutionize the current FLIM system with improved detection capabilities opening doors for new horizons. 

We present a new deep compressed imaging modality by scanning a learned illumination pattern on the sample and detecting the signal with a single-pixel detector. This new imaging modality allows a compressed sampling of the object, and thus a high imaging speed. The object is reconstructed through a deep neural network inspired by compressed sensing algorithm. We optimize the illumination pattern and the image reconstruction network by training an end-to-end auto-encoder framework. Comparing with the conventional single-pixel camera and point-scanning imaging system, we accomplish a high-speed imaging with a reduced light dosage, while preserving a high imaging quality.